Scott Sanner
Biographic Data
| ID | 5152700 |
|---|---|
| NAME | Scott Sanner |
| GIVEN NAMES | Scott |
| FAMILY NAME | Sanner |
| SIGNATURE | SANNER S |
| AFFILIATIONS | University of Toronto |
| ORCID | 0000-0001-7984-8394 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 6 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Mapping Social Change
Analyzing social change requires detecting patterns of continuity and difference over time. While time-series clustering offers a valuable approach, existing techniques are often limited by assuming fixed cluster definitions and static assignments of entities to clusters. To address these limitations, we introduce a unified framework of temporal clustering methods that allows for both dynamic cluster definitions and the transition of entities bet…
Tscluster
Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic lab…
Classification and Regression via Integer Optimization for Neighborhood Change
This article applies a method we term “predictive clustering” to cluster neighborhoods. Much of the literature in this direction is based on groupings built using intrinsic characteristics of each observation. Our approach departs from this framework by delineating clusters based on how the neighborhood’s features respond to a particular outcome of interest (e.g., income change). To do so, we leverage a classification and regression via integer o…
Reading the city through its neighbourhoods
Reading the city through its neighbourhoods
Classification and Regression via Integer Optimization for Neighborhood Change
This article applies a method we term “predictive clustering” to cluster neighborhoods. Much of the literature in this direction is based on groupings built using intrinsic characteristics of each observation. Our approach departs from this framework by delineating clusters based on how the neighborhood’s features respond to a particular outcome of interest (e.g., income change). To do so, we leverage a classification and regression via integer o…
Tscluster
Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic lab…
Mapping Social Change
Analyzing social change requires detecting patterns of continuity and difference over time. While time-series clustering offers a valuable approach, existing techniques are often limited by assuming fixed cluster definitions and static assignments of entities to clusters. To address these limitations, we introduce a unified framework of temporal clustering methods that allows for both dynamic cluster definitions and the transition of entities bet…
Artificial Intelligence (3 works) · Cluster analysis (3 works) · Computer Science (3 works) · Complex Network Analysis Techniques (2 works) · Computational and Text Analysis Methods (2 works) · Mathematics (2 works) · Artificial Intelligence (1 work) · Autoencoder (1 work) · Bayesian Methods and Mixture Models (1 work) · Biclustering (1 work)